Detection of COVID-19 using CXR and CT images using Transfer Learning and Haralick features

The COVID-19 pandemic requires the rapid isolation of infected patients. Thus, high-sensitivity radiology images could be a key technique to diagnose patients besides the polymerase chain reaction approach. Deep learning algorithms are proposed in several studies to detect COVID-19 symptoms due to the success in chest radiography image classification, cost efficiency, lack of expert radiologists, and the need for faster processing in the pandemic area. Most of the promising algorithms proposed in different studies are based on pre-trained deep learning models. Such open-source models and lack of variation in the radiology image-capturing environment make the diagnosis system vulnerable to adversarial attacks such as fast gradient sign method (FGSM) attack. This study therefore explored the potential vulnerability of pre-trained convolutional neural network algorithms to the FGSM attack in terms of two frequently used models, VGG16 and Inception-v3. Firstly, we developed two transfer learning models for X-ray and CT image-based COVID-19 classification and analyzed the performance extensively in terms of accuracy, precision, recall, and AUC. Secondly, our study illustrates that misclassification can occur with a very minor perturbation magnitude, such as 0.009 and 0.003 for the FGSM attack in these models for X-ray and CT images, respectively, without any effect on the visual perceptibility of the perturbation. In addition, we demonstrated that successful FGSM attack can decrease the classification performance to 16.67% and 55.56% for X-ray images, as well as 36% and 40% in the case of CT images for VGG16 and Inception-v3, respectively, without any human-recognizable perturbation effects in the adversarial images. Finally, we analyzed that correct class probability of any test image which is supposed to be 1, can drop for both considered models and with increased perturbation; it can drop to 0.24 and 0.17 for the VGG16 model in cases of X-ray and CT images, respectively. Thus, despite the need for data sharing and automated diagnosis, practical deployment of such program requires more robustness.

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COVID-19 Diagnosis and Severity Detection from CT-Images Using Transfer Learning and Back Propagation Neural Network

Journal of Infection and Public Health ◽

10.1016/j.jiph.2021.07.015 ◽

2021 ◽

Author(s):

A.L. Āswathy ◽

S. Anand Hareendran ◽

S.S. VinodChandra

Keyword(s):

Neural Network ◽

Transfer Learning ◽

Back Propagation ◽

Back Propagation Neural Network ◽

Ct Images

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Deep Learning Enabled Deblurring of Computed Tomography Images of Porous Media

10.2118/208665-ms ◽

2021 ◽

Author(s):

Khalid Labib Alsamadony ◽

Ertugrul Umut Yildirim ◽

Guenther Glatz ◽

Umair bin Waheed ◽

Sherif M. Hanafy

Keyword(s):

Computed Tomography ◽

Image Quality ◽

Transfer Learning ◽

Temporal Resolution ◽

Exposure Time ◽

Ct Images ◽

Loss Functions ◽

High Quality ◽

X Ray ◽

Training Images

Abstract Computed tomography (CT) is an important tool to characterize rock samples allowing quantification of physical properties in 3D and 4D. The accuracy of a property delineated from CT data is strongly correlated with the CT image quality. In general, high-quality, lower noise CT Images mandate greater exposure times. With increasing exposure time, however, more wear is put on the X-Ray tube and longer cooldown periods are required, inevitably limiting the temporal resolution of the particular phenomena under investigation. In this work, we propose a deep convolutional neural network (DCNN) based approach to improve the quality of images collected during reduced exposure time scans. First, we convolve long exposure time images from medical CT scanner with a blur kernel to mimic the degradation caused because of reduced exposure time scanning. Subsequently, utilizing the high- and low-quality scan stacks, we train a DCNN. The trained network enables us to restore any low-quality scan for which high-quality reference is not available. Furthermore, we investigate several factors affecting the DCNN performance such as the number of training images, transfer learning strategies, and loss functions. The results indicate that the number of training images is an important factor since the predictive capability of the DCNN improves as the number of training images increases. We illustrate, however, that the requirement for a large training dataset can be reduced by exploiting transfer learning. In addition, training the DCNN on mean squared error (MSE) as a loss function outperforms both mean absolute error (MAE) and Peak signal-to-noise ratio (PSNR) loss functions with respect to image quality metrics. The presented approach enables the prediction of high-quality images from low exposure CT images. Consequently, this allows for continued scanning without the need for X-Ray tube to cool down, thereby maximizing the temporal resolution. This is of particular value for any core flood experiment seeking to capture the underlying dynamics.

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Deep Transfer Learning Pipelines with Apache Spark and Keras TensorFlow combined with Logistic Regression to Detect COVID-19 in Chest CT Images

Walailak Journal of Science and Technology (WJST) ◽

10.48048/wjst.2021.13109 ◽

2021 ◽

Vol 18 (11) ◽

Author(s):

Houssam BENBRAHIM ◽

Hanaa HACHIMI ◽

Aouatif AMINE

Keyword(s):

Neural Network ◽

Logistic Regression ◽

Convolutional Neural Network ◽

Transfer Learning ◽

Virus Disease ◽

Ct Images ◽

Chest Ct ◽

Apache Spark ◽

Difficult Situation ◽

Novel Coronavirus

The SARS-CoV-2 (COVID-19) has propagated rapidly around the world, and it became a global pandemic. It has generated a catastrophic effect on public health. Thus, it is crucial to discover positive cases as early as possible to treat touched patients fastly. Chest CT is one of the methods that play a significant role in diagnosing 2019-nCoV acute respiratory disease. The implementation of advanced deep learning techniques combined with radiological imaging can be helpful for the precise detection of the novel coronavirus. It can also be assistive to surmount the difficult situation of the lack of medical skills and specialized doctors in remote regions. This paper presented Deep Transfer Learning Pipelines with Apache Spark and KerasTensorFlow combined with the Logistic Regression algorithm for automatic COVID-19 detection in chest CT images, using Convolutional Neural Network (CNN) based models VGG16, VGG19, and Xception. Our model produced a classification accuracy of 85.64, 84.25, and 82.87 %, respectively, for VGG16, VGG19, and Xception. HIGHLIGHTS Deep Transfer Learning Pipelines with Apache Spark and Keras TensorFlow combined with Logistic Regression using CT images to screen for Corona Virus Disease (COVID-19) Automatic detection of COVID-19 in chest CT images Convolutional Neural Network (CNN) based models VGG16, VGG19, and Xception to predict COVID-19 in Computed Tomography image GRAPHICAL ABSTRACT

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Deep Transfer Learning Application for Automated Ischemic Classification in Posterior Fossa CT Images

International Journal of Advanced Computer Science and Applications ◽

10.14569/ijacsa.2019.0100859 ◽

2019 ◽

Vol 10 (8) ◽

Author(s):

Anis Azwani Muhd Suberi ◽

Wan Nurshazwani ◽

Razali Tomari ◽

Ain Nazari ◽

Mohd Norzali ◽

...

Keyword(s):

Transfer Learning ◽

Posterior Fossa ◽

Ct Images

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COVID-19 detection on IBM quantum computer with classical-quantum transfer learning

10.1101/2020.11.07.20227306 ◽

2020 ◽

Author(s):

Erdi Acar ◽

İhsan Yilmaz

Keyword(s):

Machine Learning ◽

Transfer Learning ◽

Ct Images ◽

Nucleic Acid Amplification ◽

World Health ◽

Quantum Computers ◽

Data Set ◽

Small Quantum ◽

Short Time

AbstractDiagnose the infected patient as soon as possible in the coronavirus 2019 (COVID-19) outbreak which is declared as a pandemic by the world health organization (WHO) is extremely important. Experts recommend CT imaging as a diagnostic tool because of the weak points of the nucleic acid amplification test (NAAT). In this study, the detection of COVID-19 from CT images, which give the most accurate response in a short time, was investigated in the classical computer and firstly in quantum computers. Using the quantum transfer learning method, we experimentally perform COVID-19 detection in different quantum real processors (IBMQx2, IBMQ-London and IBMQ-Rome) of IBM, as well as in different simulators (Pennylane, Qiskit-Aer and Cirq). By using a small number of data sets such as 126 COVID-19 and 100 Normal CT images, we obtained a positive or negative classification of COVID-19 with 90% success in classical computers, while we achieved a high success rate of 94-100% in quantum computers. Also, according to the results obtained, machine learning process in classical computers requiring more processors and time than quantum computers can be realized in a very short time with a very small quantum processor such as 4 qubits in quantum computers. If the size of the data set is small; Due to the superior properties of quantum, it is seen that according to the classification of COVID-19 and Normal, in terms of machine learning, quantum computers seem to outperform traditional computers.

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